Most companies in both the service and goods industries foster relationships with the end users of their products and/or services. Such relationships may begin early on in the process, such as during a marketing campaign, and the relationships may continue for quite some time. Occasionally relationships with customers result in a customer purchase of a good and/or service. To provide quality customer relations, companies often track data related to the growth of customer relationships. For example, some companies track whether or not correspondence has been sent to a customer (e.g., a catalog and/or an email). Separately, some companies also track customer purchases over time and the customer's satisfaction with a good and/or service. For example, after a sale is completed some of these companies track customer satisfaction according to a customer's responses in a survey.
However, companies do not track, throughout the relationship between the customer and the company, the totality of the interactions. These companies, instead, segment their customer relations into separate marketing and customer satisfaction arenas. Accordingly, the companies fail to identify trends regarding the customer interactions and sales. Similarly, the companies fail to identify the relative success rates of trends from the interactions among specific demographics of customers.
Known visualization tools, implemented by these companies for reviewing the success of an interaction with a customer are cumbersome and difficult to readily interpret. Tables, containing large numbers entries regarding customer data (e.g., a customer's date of birth and their sales history), remain untransformed tables. Identifying and tracking trends among tables of categorized data or graphs of categorized data requires significant individual analysis and time.
Taking the time to interpret and analyze the categorized data stymies sales and the ability of the companies to recognize successful and unsuccessful trends in their respective customer relationships. Furthermore, the data presented in known visualization tools fails to acknowledge time relationships or hierarchical relationships that clearly exist in the interaction data. For example, the data presented does not acknowledge an interaction that occurs, in time, immediately before or immediately after the data under analysis in a table or graph.
Lag time between interactions, sales, and any analysis of the interactions further is detrimental to the companies. Information regarding the interactions is reviewed or considered relevant only after a sale is completed. Such information is culled, categorized, and only then transformed into a categorized graph and/or table for the review of the analytics team of the company. The slow analysis process of these companies further hinders their ability to quickly respond to trends in the customer interactions that could otherwise save the company money or generate additional revenue for the company.
Furthermore, the known visualization tools are simply tools that provide the data as is. After analysis of the data, the company or an employee might suggest that a portion of the represented data should be further analyzed according to specific criteria using a different tool. However, the time spent in recognition of a trend, in generation of interest in a possible data correlation, and in translation to the different tool for the analysis is again time lost that could instead be used to respond to identified trends in the marketplace.
Accordingly, a there is a need for a method, a system, and a visualization tool that is capable of selecting data for presentation in the visualization tool according to mined data regarding a customer's journey, including the customer's habits and characteristics, and dynamically presenting the selected data in an intuitive manner that further allows for analysis and additional contextual data inquiries.